Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle Applications

Abstract

It is a common intuition from battery experts that many shape features in the voltage profile image contain abundant information related to battery performance. However, such features are often too subtle for a human to extract by eye inspection and further correlate with battery performance. Using long cycling data from hundreds of large-format pouch cells and a total of 2 million cycles tested over 1000 days, we demonstrate here for the first time that it is advantageous to accurately predict the capacity and remaining useful life in real time by learning battery voltage profile images rather than voltage values. A strategy of end-to-end performance prediction of large-format battery cells is thus demonstrated to be feasible using only a few of the previous cycles at any given time point during the cycling test. Our work paves the way toward the application of machine learning for real-time battery performance prediction and regulation for electric vehicle applications.